The present invention is generally directed to a system and method for evaluating particular performance aspects of a business. More particularly, the present invention is directed to the use of benchmarking questionnaires and rule-induction based logical processes to rank and position business performance in specific areas by identifying relevant criteria or key discriminators that drive toward specific levels of performance.
Benchmarking is a well-known technique employed by consultants, advisors and like business analysts for assisting in the improvement of corporate performance by comparing the performance of an entity within the same organization or against other organizations. Benchmarking is typically associated with comparisons of overhead and operating costs as part of an organization's efforts to improve its competitive cost base in order to enhance its overall performance.
The concept underlying benchmarking is that current organizational performance in defined activities can be compared internally and/or externally. For example, external benchmarking can be achieved by: (i) direct information exchange under special arrangements with chosen individual benchmarking professionals having a particular knowledge concerning the practices of peers in a relevant area or industry, or (ii) participation in or subscription to an independent database of the comparable performance of a number of organizations (with their identities hidden). These databases enable comparison with a so-called “best in class” among the participating organizations.
While these known uses of benchmarking may be helpful in identifying operational processes and financial measures indicative of overall corporate performance and health, these known applications of benchmarking do not capture all the main, relevant dimensions of a company's performance. That is, these traditional benchmarking techniques identify a business's outputs as compared to the outputs of other businesses which are not necessarily indicators of future business drivers. Thus, past period sales revenue, for example, may provide no indication of future customer satisfaction that may arise, for example, from the length of re-order cycles and/or new product development.
While these quantitative approaches to benchmarking may in the long run help improve overall performance, such approaches are difficult to prepare and interpret and require undue reliance on third parties. The problems with benchmarking are especially acute in benchmarking business performance. Despite all the expertise available in the area of business performance, that expertise is recorded mainly in the form of experiences or cases. The knowledge from the facts and outcomes that comprise these cases is not easily converted into a set of rules that can be used to predict future business performance outcomes.
Because of the existence of numerous competing objectives, the challenge is to develop a coherent set of rules that is predictive of future success or behavior. Artificial Intelligence (“AI”) has been used outside the business performance area to help extract predictive rules from a set of data. There are many approaches that fall under the rubric of AI: neural networks, rule-based systems, genetic algorithms, symbolic learning, etc. Rule-based systems have been perceived to be easily applicable to decision making in structured business domains. However, the design, applicability and use of these rule-based systems is not well suited to interpreting business performance data due to a lack of a general and well-accepted methodologies in rule-based systems design, and a high dependence on somewhat rudimentary models (such as the rule-based formalism).
Other more recent tools such as neural networks and genetic algorithms use very different approaches since they need to be trained with data sets from which they derive patterns. These new generation tools deal much more easily with unstructured decision making since their rationale is to guide the end users by enhancing their judgment rather than supplanting it.
Accordingly, a need exists for a benchmarking system and method that automatically focuses on a wide range of qualitative or non-financial measures of performance rather than exclusively on precise quantitative outputs (e.g., operating costs and overhead). A further need exists for a benchmarking system and method that is suitable for automatically defining or identifying predictive rules or patterns in a set of business performance data. A further need exists for such a benchmarking system and method that incorporate machine learning in the form of artificial intelligence using an appropriate logic based system to extract and interpret rules or patterns for a database of performance data for a plurality of companies. Such a benchmarking system and method must be capable of presenting the benchmarking results in an easy to understand format to provide a clear and strong guide to the action to be taken and agreed upon by a company's managers and leaders.